code: def test_padding_and_spec(self):
      if ok_ljspeech:
          dataloader, dataset = self._create_dataloader(1, 1, 0)
  
          for i, data in enumerate(dataloader):
              if i == self.max_loader_iter:
                  break
              text_input = data[0]
              text_lengths = data[1]
              speaker_name = data[2]
              linear_input = data[3]
              mel_input = data[4]
              mel_lengths = data[5]
              stop_target = data[6]
              item_idx = data[7]
  
              # check mel_spec consistency
              wav = np.asarray(
                  self.ap.load_wav(item_idx[0]), dtype=np.float32
              )
              mel = self.ap.melspectrogram(wav).astype("float32")
              mel = torch.FloatTensor(mel).contiguous()
              mel_dl = mel_input[0]
              # NOTE: Below needs to check == 0 but due to an unknown reason
              # there is a slight difference between two matrices.
              # TODO: Check this assert cond more in detail.
              assert abs(mel.T - mel_dl).max() < 1e-5, abs(
                  mel.T - mel_dl
              ).max()
  
              # check mel-spec correctness
              mel_spec = mel_input[0].cpu().numpy()
              wav = self.ap.inv_melspectrogram(mel_spec.T)
              self.ap.save_wav(wav, OUTPATH + "/mel_inv_dataloader.wav")
              shutil.copy(
                  item_idx[0], OUTPATH + "/mel_target_dataloader.wav"
              )
  
              # check linear-spec
              linear_spec = linear_input[0].cpu().numpy()
              wav = self.ap.inv_spectrogram(linear_spec.T)
              self.ap.save_wav(
                  wav, OUTPATH + "/linear_inv_dataloader.wav"
              )
              shutil.copy(
                  item_idx[0], OUTPATH + "/linear_target_dataloader.wav"
              )
  
              # check the last time step to be zero padded
              assert linear_input[0, -1].sum() != 0
              assert linear_input[0, -2].sum() != 0
              assert mel_input[0, -1].sum() != 0
              assert mel_input[0, -2].sum() != 0
              assert stop_target[0, -1] == 1
              assert stop_target[0, -2] == 0
              assert stop_target.sum() == 1
              assert len(mel_lengths.shape) == 1
              assert mel_lengths[0] == linear_input[0].shape[0]
              assert mel_lengths[0] == mel_input[0].shape[0]
  
          # Test for batch size 2
          dataloader, dataset = self._create_dataloader(2, 1, 0)
  
          for i, data in enumerate(dataloader):
              if i == self.max_loader_iter:
                  break
              text_input = data[0]
              text_lengths = data[1]
              speaker_name = data[2]
              linear_input = data[3]
              mel_input = data[4]
              mel_lengths = data[5]
              stop_target = data[6]
              item_idx = data[7]
  
              if mel_lengths[0] > mel_lengths[1]:
                  idx = 0
              else:
                  idx = 1
  
              # check the first item in the batch
              assert linear_input[idx, -1].sum() != 0
              assert linear_input[idx, -2].sum() != 0, linear_input
              assert mel_input[idx, -1].sum() != 0
              assert mel_input[idx, -2].sum() != 0, mel_input
              assert stop_target[idx, -1] == 1
              assert stop_target[idx, -2] == 0
              assert stop_target[idx].sum() == 1
              assert len(mel_lengths.shape) == 1
              assert mel_lengths[idx] == mel_input[idx].shape[0]
              assert mel_lengths[idx] == linear_input[idx].shape[0]
  
              # check the second itme in the batch
              assert linear_input[1 - idx, -1].sum() == 0
              assert mel_input[1 - idx, -1].sum() == 0
              assert stop_target[1, mel_lengths[1] - 1] == 1
              assert stop_target[1, mel_lengths[1] :].sum() == 0
              assert len(mel_lengths.shape) == 1
  
file_path: TTS/tests/test_loader.py
imports: import os
  import shutil
  import unittest
  import numpy
  import torch
  from tests import get_tests_input_path, get_tests_output_path
  from torch.utils.data import DataLoader
  from TTS.tts.datasets import TTSDataset
  from TTS.tts.datasets.preprocess import ljspeech
  from TTS.utils.audio import AudioProcessor
  from TTS.utils.io import load_config
label_desc: Use image node_id 5 for calling the TestTTSDataset obj's underlying member method code with example usage: obj.test_padding_and_spec() without return types